scholarly journals The effects of a globin blocker on the resolution of 3’mRNA sequencing data in porcine blood

2019 ◽  
Author(s):  
Kyu-Sang Lim ◽  
Qian Dong ◽  
Pamela Renate Moll ◽  
Jana Vitkovska ◽  
Gregor Wiktorin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Globin Genes ◽  
Sequencing Data ◽  
Globin Mrna ◽  
Additional Advantage ◽  
Phenotypic Differences ◽  
Mrna Sequencing ◽  
Porcine Blood

Abstract Background : Gene expression profiling in blood is a potential source of biomarkers to evaluate or predict phenotypic differences between pigs but is expensive and inefficient because of the high abundance of globin mRNA in porcine blood. These limitations can be overcome by the use of 3’mRNA sequencing combined with a method to deplete or block the processing of globin mRNA prior to or during library construction. Here, we validated the effectiveness of a novel specific globin blocker (GB) that is included in the library preparation step of 3’mRNA sequencing. Results : Four concentrations of the GB were applied to RNA samples from two pigs. The GB significantly reduced the proportion of globin reads ( P = 0.005) and increased the number of detectable non-globin genes. The highest evaluated concentration (C1) of the GB resulted in the largest reduction of globin reads (from 56.4 to 10.1%). The second highest concentration C2, showed very similar globin depletion rates (12 %) as C1 but a better correlation of the expression of non-globin genes between GB and non-GB ( r = 0.98), and allowed the expression of an additional 1,295 non-globin genes to be detected. Concentration C2 was applied in the rest of the study. The distribution of the percentage of globin reads for non-GB (n=184) and GB (n=189) samples clearly showed the effects of the GB on reducing globin reads, in particular for HBB . The proportion of globin reads that remained in GB samples was found to be positively correlated with reticulocyte count of the blood sample ( P < 0.001). Conclusions : The GB for 3’mRNA sequencing is a useful tool in the quantification of whole gene expression profiles in porcine blood. The GB reduced the proportion of globin reads, thereby increasing the efficiency of sequencing non-globin mRNA. The evaluated GB method has as additional advantage that it does not require an additional step prior to or during library creation.

scholarly journals The effects of a globin blocker on the resolution of 3’mRNA sequencing data in porcine blood

BMC Genomics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Kyu-Sang Lim ◽  
Qian Dong ◽  
Pamela Moll ◽  
Jana Vitkovska ◽  
Gregor Wiktorin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Data Sets ◽  
Globin Genes ◽  
Sequencing Data ◽  
Globin Mrna ◽  
Data Set ◽  
Mrna Sequencing ◽  
Porcine Blood

Abstract Background Gene expression profiling in blood is a potential source of biomarkers to evaluate or predict phenotypic differences between pigs but is expensive and inefficient because of the high abundance of globin mRNA in porcine blood. These limitations can be overcome by the use of QuantSeq 3’mRNA sequencing (QuantSeq) combined with a method to deplete or block the processing of globin mRNA prior to or during library construction. Here, we validated the effectiveness of QuantSeq using a novel specific globin blocker (GB) that is included in the library preparation step of QuantSeq. Results In data set 1, four concentrations of the GB were applied to RNA samples from two pigs. The GB significantly reduced the proportion of globin reads compared to non-GB (NGB) samples (P = 0.005) and increased the number of detectable non-globin genes. The highest evaluated concentration (C1) of the GB resulted in the largest reduction of globin reads compared to the NGB (from 56.4 to 10.1%). The second highest concentration C2, which showed very similar globin depletion rates (12%) as C1 but a better correlation of the expression of non-globin genes between NGB and GB (r = 0.98), allowed the expression of an additional 1295 non-globin genes to be detected, although 40 genes that were detected in the NGB sample (at a low level) were not present in the GB library. Concentration C2 was applied in the rest of the study. In data set 2, the distribution of the percentage of globin reads for NGB (n = 184) and GB (n = 189) samples clearly showed the effects of the GB on reducing globin reads, in particular for HBB, similar to results from data set 1. Data set 3 (n = 84) revealed that the proportion of globin reads that remained in GB samples was significantly and positively correlated with the reticulocyte count in the original blood sample (P < 0.001). Conclusions The effect of the GB on reducing the proportion of globin reads in porcine blood QuantSeq was demonstrated in three data sets. In addition to increasing the efficiency of sequencing non-globin mRNA, the GB for QuantSeq has an advantage that it does not require an additional step prior to or during library creation. Therefore, the GB is a useful tool in the quantification of whole gene expression profiles in porcine blood.

scholarly journals The effects of a globin blocker on the resolution of 3’mRNA sequencing data in porcine blood

2019 ◽  
Author(s):  
Kyu-Sang Lim ◽  
Qian Dong ◽  
Pamela Renate Moll ◽  
Jana Vitkovska ◽  
Gregor Wiktorin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Data Sets ◽  
Sequencing Data ◽  
Data Set ◽  
Phenotypic Differences ◽  
Mrna Sequencing ◽  
Porcine Blood ◽  
Preparation Step

Abstract Background Gene expression profiling in blood is a potential source of biomarkers to evaluate or predict phenotypic differences between pigs but is expensive and inefficient because of the high abundance of hemoglobin (HB) mRNA in porcine blood. These limitations can be overcome by the use of QuantSeq 3’mRNA sequencing (QuantSeq) combined with a method to deplete or block the processing of HB mRNA prior to or during library construction. Here, we validated the effectiveness of QuantSeq using a novel specific globin blocker (GB) that is included in the library preparation step of QuantSeq. Results In data set 1, four concentrations of the GB were applied to RNA samples from two pigs. The GB significantly reduced the proportion of HB reads compared to non-GB (NGB) samples (P = 0.005) and increased the number of detectable non-HB genes. The second highest concentration C2, which showed very similar globin depletion rates (from 56.4 to 12%) as C1 but a better correlation of the expression of non-HB genes between NGB and GB (r = 0.98), allowed the expression of an additional 1,295 non-HB genes to be detected, although 40 genes that were detected in the NGB sample (at a low level) were not present in the GB library. Concentration C2 was applied in the rest of the study. In data set 2, the distribution of the percentage of HB reads for NGB (n=184) and GB (n=189) samples clearly showed the effects of the GB on reducing HB reads. Data set 3 (n=84) revealed that the proportion of HB reads that remained in GB samples was significantly and positively correlated with the reticulocyte count in the original blood sample (P < 0.001). Conclusions The effect of the GB on reducing the proportion of HB reads in porcine blood QuantSeq was demonstrated in three data sets. In addition to increasing the efficiency of sequencing non-HB mRNA, the GB for QuantSeq has as advantage that it does not require an additional step prior to or during library creation. Therefore, the GB is a useful tool in the quantification of whole gene expression profiles in porcine blood.

scholarly journals LncGSEA: a versatile tool to infer lncRNA associated pathways from large-scale cancer transcriptome sequencing data

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yanan Ren ◽  
Ting-You Wang ◽  
Leah C. Anderton ◽  
Qi Cao ◽  
Rendong Yang
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Clinical Samples ◽  
Sequencing Data ◽  
Multiple Cancer ◽  
Regulatory Pathways ◽  
Cancer Transcriptome ◽  
Versatile Tool

Abstract Background Long non-coding RNAs (lncRNAs) are a growing focus in cancer research. Deciphering pathways influenced by lncRNAs is important to understand their role in cancer. Although knock-down or overexpression of lncRNAs followed by gene expression profiling in cancer cell lines are established approaches to address this problem, these experimental data are not available for a majority of the annotated lncRNAs. Results As a surrogate, we present lncGSEA, a convenient tool to predict the lncRNA associated pathways through Gene Set Enrichment Analysis of gene expression profiles from large-scale cancer patient samples. We demonstrate that lncGSEA is able to recapitulate lncRNA associated pathways supported by literature and experimental validations in multiple cancer types. Conclusions LncGSEA allows researchers to infer lncRNA regulatory pathways directly from clinical samples in oncology. LncGSEA is written in R, and is freely accessible at https://github.com/ylab-hi/lncGSEA.

Gene Expression Profiles Involved in γ to β Globin Gene Switching during Erythroid Maturation.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 820-820
Author(s):  
Wei Li ◽  
Betty S. Pace
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Globin Gene ◽  
Gene Expression Profiles ◽  
Phase System ◽  
Mrna Levels ◽  
Erythroid Progenitors ◽  
Globin Mrna ◽  
Gene Switching ◽  
Globin Gene Expression

Abstract The design and evaluation of therapies for sickle cell disease (SCD) rely on our understanding of hemoglobin accumulation during erythropoiesis and sequential globin gene expression (ε → Gγ → Aγ → δ → β) during development. To gain insights into globin gene switching, we completed time course micorarray analyses of erythroid progenitors to identify trans-factors involved in γ gene activation. Studies were completed to map the pattern of γ and β globin gene expression in progenitors grown from normal peripheral blood mononuclear cells. We compared cells grown in a 2-phase (phase 1, d0-6: SCF, IL-3, IL-6, and GM-CSF and phase 2, d7-25: SCF and EPO) vs. 1-phase (d0-34: SCF, IL-3, and EPO) liquid culture system. From day 0 to 34 in either system cell viability remained &gt;99%. Total RNA was isolated using Trizol and column cleanup (Qiagen). Globin mRNA levels were measured at 2–3 day intervals by quantitative PCR (qPCR). In the 2-phase system γ-globin mRNA&gt;β-globin mRNA up to d14, 4 days of approximately equal expression then β mRNA &gt; γ mRNA by d20. By contrast, in 1-phase studies there was a rapid switch around d20(see graph). We speculate that this difference may be due to the early addition of EPO on d0 therefore we continued our detailed analysis in this system. To confirm that our in vitro system recapitulates in vivo gene expression patterns, we completed studies to ascertain Gγ - vs. Aγ globin mRNA levels. The normalized Gγ:Aγ ratio decreased from ~3:1 on d7 to ~1:1 by d34; These findings were confirmed using two sets of Gγ and Aγ globin primers. We concluded that the 1-phase system recapitulated normal γ/β globin switching and that gene profiling studies to identify the trans-factor involved in switching mechanisms were feasible. We used Discover oligo chips (ArrayIt, Sunnyvale, CA) containing 380 human genes selected from 30 major functional groups including hematopoiesis. To aide interpretation of chip data, cell populations were rated morphologically using Giemsa stained cytospin preps. From d16 on we observed an increase in late erythroid progenitors (normoblasts) from 1% to 71% by d31. After verifying RNA quality by gel inspection of ribosomal molecules, we prepared Cy3 and Cy5 probes for early and late time-point RNA samples respectively. Chip analysis was performed at several time points but d0/21, d7/21, and d21/28 were most informative. Based on Axon GenePixPro 6.0 and Acuity 4.0 software analysis we found the following genes with &gt;1.5-fold change in expression profile (shown as down-regulated/up-regulated genes): d0/21: 33/73, d7/21: 13/25, and d21/28:35/26. Principal component analysis (PCA), hierarchical clusters and self organizing maps were constructed. Gene profiles were correlated with the γ/β switching curve using d7 (γ &gt;β), d21 (γ ~ β), and d28 (γ &lt;β) data. Hematopoietic dataset analysis at d21 revealed 4 candidate γ-globin gene activators including v-myb, upsteam binding transfactor -RNApol1 and 2 zinc finger proteins. Analysis of a d28 dataset revealed 12 proteins involved in γ-globin gene silencing including IL-3, SCF, MAPKKK3, v-raf-1, ATF-2, and glucocorticoid receptor DNA binding factor 1 among others. Gene expression profiles will be validated using qPCR and promising candidates will be tested by forced expression in transient and stable reporter systems. Figure Figure

scholarly journals Effects of Globin mRNA Reduction Methods on Gene Expression Profiles from Whole Blood

2006 ◽  
Vol 8 (5) ◽  
pp. 551-558 ◽  
Author(s):  
Jinny Liu ◽  
Elizabeth Walter ◽  
David Stenger ◽  
Dzung Thach
Keyword(s):  
Gene Expression ◽  
Whole Blood ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Globin Mrna ◽  
Reduction Methods

scholarly journals G2S3: a gene graph-based imputation method for single-cell RNA sequencing data

2020 ◽  
Author(s):  
Weimiao Wu ◽  
Qile Dai ◽  
Yunqing Liu ◽  
Xiting Yan ◽  
Zuoheng Wang
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Sequencing Data ◽  
High Data ◽  
Study Gene Expression ◽  
Single Cell Rna Sequencing ◽  
Novel Method

AbstractSingle-cell RNA sequencing provides an opportunity to study gene expression at single-cell resolution. However, prevalent dropout events result in high data sparsity and noise that may obscure downstream analyses. We propose a novel method, G2S3, that imputes dropouts by borrowing information from adjacent genes in a sparse gene graph learned from gene expression profiles across cells. We applied G2S3 and other existing methods to seven single-cell datasets to compare their performance. Our results demonstrated that G2S3 is superior in recovering true expression levels, identifying cell subtypes, improving differential expression analyses, and recovering gene regulatory relationships, especially for mildly expressed genes.

Sequencing dropout-and-batch effect normalization for single-cell mRNA profiles: a survey and comparative analysis

2020 ◽  
Author(s):  
Tian Lan ◽  
Gyorgy Hutvagner ◽  
Qing Lan ◽  
Tao Liu ◽  
Jinyan Li
Keyword(s):  
Single Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Batch Effect ◽  
Sequencing Data ◽  
Accurate Analysis ◽  
Mrna Sequencing ◽  
Normalization Methods ◽  
Future Improvement ◽  
Better Than

Abstract Single-cell mRNA sequencing has been adopted as a powerful technique for understanding gene expression profiles at the single-cell level. However, challenges remain due to factors such as the inefficiency of mRNA molecular capture, technical noises and separate sequencing of cells in different batches. Normalization methods have been developed to ensure a relatively accurate analysis. This work presents a survey on 10 tools specifically designed for single-cell mRNA sequencing data preprocessing steps, among which 6 tools are used for dropout normalization and 4 tools are for batch effect correction. In this survey, we outline the main methodology for each of these tools, and we also compare these tools to evaluate their normalization performance on datasets which are simulated under the constraints of dropout inefficiency, batch effect or their combined effects. We found that Saver and Baynorm performed better than other methods in dropout normalization, in most cases. Beer and Batchelor performed better in the batch effect normalization, and the Saver–Beer tool combination and the Baynorm–Beer combination performed better in the mixed dropout-and-batch effect normalization. Over-normalization is a common issue occurred to these dropout normalization tools that is worth of future investigation. For the batch normalization tools, the capability of retaining heterogeneity between different groups of cells after normalization can be another direction for future improvement.

scholarly journals Subtle left-right asymmetry of gene expression profiles in embryonic and foetal human brains

2018 ◽  
Author(s):  
Carolien G.F. de Kovel ◽  
Steven N. Lisgo ◽  
Simon E. Fisher ◽  
Clyde Francks
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Adult Brain ◽  
Human Embryos ◽  
Sequencing Data ◽  
Brain Organization ◽  
Central Nervous Systems ◽  
Left Right Asymmetry ◽  
Human Brains

AbstractLeft-right laterality is an important aspect of human brain organization for which the genetic basis is poorly understood. Using RNA sequencing data we contrasted gene expression in left- and right-sided samples from several structures of the anterior central nervous systems of post mortem human embryos and fetuses. While few individual genes stood out as significantly lateralized, most structures showed evidence of laterality of their overall transcriptomic profiles. These left-right differences showed overlap with age-dependent changes in expression, indicating lateralized maturation rates, but not consistently in left-right orientation over all structures. Brain asymmetry may therefore originate in multiple locations, or if there is a single origin, it is earlier than 5 weeks post conception, with structure-specific lateralized processes already underway by this age. This pattern is broadly consistent with the weak correlations reported between various aspects of adult brain laterality, such as language dominance and handedness.

Expression Profile of PIP Kinases During In Vitro Human Erythropoiesis

Blood ◽  
2010 ◽  
Vol 116 (21) ◽  
pp. 2079-2079
Author(s):  
Tânia Regina Zaccariotto ◽  
Carolina Lanaro ◽  
Dulcinéia Martins Albuquerque ◽  
Magnun N N Santos ◽  
Marcos André Cavalcanti Bezerra ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Culture ◽  
Expression Profile ◽  
Expression Profiles ◽  
Globin Gene ◽  
Gene Expression Profiles ◽  
Erythroid Differentiation ◽  
Globin Genes ◽  
Hb H Disease

Abstract Abstract 2079 Phosphatidylinositol phosphate kinases (PIPKs) are a family of lipid kinase enzymes that produce the second messenger PI4,5P2 (phosphatidylinositol 4,5-biphosphate), which plays an important role in the regulation of a variety of cellular activities, including gene expression. PIPKs are classified into 3 subfamilies — PIPK I (a, b, g), PIPK II (a, b, g) and PIPK III — which are functionally distinct and are located in different subcellular compartments. In a recent study in our laboratory, the PIPKIIa gene was differentially expressed in reticulocytes from 2 siblings with hemoglobin (Hb) H disease who had the same genotype (-a3.7/–SEA). Expression of both the PIPKIIa and b-globin genes were higher in the patient with the higher Hb H level, suggesting a possible relationship between PIPKIIa and the production of globins, particularly b-globin. In light of these findings, the aim of this study was to determine the gene expression profiles of PIPKs (I and II - with their isoforms a, b and g - and III) during erythropoiesis in peripheral blood hematopoietic CD34+ cell culture from 11 healthy volunteers and 6 patients with hemoglobinopathies [2 with a-thalassemia (Hb H disease), 2 with b-thalassemia (homozygous for the IVS-I-6-T-C mutation) and 2 with sickle cell anemia] using quantitative real time PCR (qRT-PCR) and to compare these profiles with the gene expression profiles of a-, b- and g-globins on the 7th, 10th and 13th days of the erythroid culture. In the cell culture from the normal group, expression of the PIPKIIa and other PIPK genes increased during erythroid differentiation, coinciding with the expression profiles of globin genes and showing in particular that a-globin has a significant effect on PIPKIIa (p<0.0001), as the PIPKIIa on a-globin gene (p=0.0002). In the patients, the expression profile of the PIPKIIa gene also increased during differentiation, whereas the results for the other PIPK genes varied. However, mRNA levels differed between patients, indicating greater complexity in individuals with hemoglobinopathies. PIPKIIa expression level was elevated in the culture from one of the a-thalassemia patients (approximately 12 times higher than in the corresponding control) but was lower than the control in one of the b-thalassemia patients. Expression levels of this gene also varied among sickle cell patients. This is the first study of the gene expression profiles of these kinases during in vitro human erythropoiesis. We identified a standard pattern of gene expression for PIPKs, and PIPKIIa in particular, a gradual increase in expression during erythroid differentiation, similar to the pattern for globin genes. This suggests that PI4,5P2, as an important secondary messenger involved in the regulation of gene expression, may play an important role in the regulation of globin gene expression and the normal process of Hb synthesis in red blood cells. Although our results varied between patients, highlighting the complexity of the regulatory systems involved in Hb production, they reinforce the hypothesis of a relationship between PIPKIIa and globin expression. This work was supported by FAPESP, CNPq and CAPES. Disclosures: No relevant conflicts of interest to declare.

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